Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations52413
Missing cells69744
Missing cells (%)8.3%
Duplicate rows1296
Duplicate rows (%)2.5%
Total size in memory8.8 MiB
Average record size in memory176.3 B

Variable types

Numeric14
Categorical2

Alerts

Dataset has 1296 (2.5%) duplicate rowsDuplicates
14616_FERM0101.DO_2_PV is highly imbalanced (> 99.9%)Imbalance
14616_FERM0101.PUMP_1_PV is highly imbalanced (99.9%)Imbalance
14616_FERM0101.Agitation_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.Air_Sparge_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.Biocontainer_Pressure_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.DO_1_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.DO_2_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.Gas_Overlay_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.Load_Cell_Net_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.pH_1_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.pH_2_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.PUMP_1_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.PUMP_1_TOTAL has 4359 (8.3%) missing valuesMissing
14616_FERM0101.PUMP_2_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.PUMP_2_TOTAL has 4359 (8.3%) missing valuesMissing
14616_FERM0101.Single_Use_DO_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.Single_Use_pH_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.Temperatura_PV has 4359 (8.3%) missing valuesMissing
14616_FERM0101.pH_1_PV is highly skewed (γ1 = -39.49908564)Skewed
14616_FERM0101.Agitation_PV has 25580 (48.8%) zerosZeros
14616_FERM0101.Air_Sparge_PV has 44872 (85.6%) zerosZeros
14616_FERM0101.DO_1_PV has 37608 (71.8%) zerosZeros
14616_FERM0101.Gas_Overlay_PV has 19158 (36.6%) zerosZeros
14616_FERM0101.Load_Cell_Net_PV has 5713 (10.9%) zerosZeros
14616_FERM0101.PUMP_1_TOTAL has 5004 (9.5%) zerosZeros
14616_FERM0101.PUMP_2_PV has 42155 (80.4%) zerosZeros
14616_FERM0101.PUMP_2_TOTAL has 11511 (22.0%) zerosZeros

Reproduction

Analysis started2024-09-29 18:19:53.738884
Analysis finished2024-09-29 18:20:10.897374
Duration17.16 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

14616_FERM0101.Agitation_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct730
Distinct (%)1.5%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean27.403867
Minimum0
Maximum80
Zeros25580
Zeros (%)48.8%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:10.945501image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q380
95-th percentile80
Maximum80
Range80
Interquartile range (IQR)80

Descriptive statistics

Standard deviation34.856413
Coefficient of variation (CV)1.2719524
Kurtosis-1.29574
Mean27.403867
Median Absolute Deviation (MAD)0
Skewness0.73694698
Sum1316865.4
Variance1214.9695
MonotonicityNot monotonic
2024-09-29T20:20:11.021095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25580
48.8%
80 13771
26.3%
20 6831
 
13.0%
36 956
 
1.8%
48 96
 
0.2%
40 87
 
0.2%
44 4
 
< 0.1%
28 4
 
< 0.1%
72 3
 
< 0.1%
56.57600098 2
 
< 0.1%
Other values (720) 720
 
1.4%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
0 25580
48.8%
19.37707849 1
 
< 0.1%
20 6831
 
13.0%
20.15528551 1
 
< 0.1%
20.1596206 1
 
< 0.1%
20.23029227 1
 
< 0.1%
20.29454404 1
 
< 0.1%
20.59187955 1
 
< 0.1%
20.61428528 1
 
< 0.1%
20.87142792 1
 
< 0.1%
ValueCountFrequency (%)
80 13771
26.3%
79.99913088 1
 
< 0.1%
79.9976832 1
 
< 0.1%
79.94213076 1
 
< 0.1%
79.89445095 1
 
< 0.1%
79.79949191 1
 
< 0.1%
79.76339559 1
 
< 0.1%
79.71484019 1
 
< 0.1%
79.68511831 1
 
< 0.1%
79.6695619 1
 
< 0.1%

14616_FERM0101.Air_Sparge_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct3183
Distinct (%)6.6%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean2.6985708
Minimum0
Maximum160.17427
Zeros44872
Zeros (%)85.6%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:11.095496image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16.384068
Maximum160.17427
Range160.17427
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.930932
Coefficient of variation (CV)4.4212039
Kurtosis22.372999
Mean2.6985708
Median Absolute Deviation (MAD)0
Skewness4.6918424
Sum129677.12
Variance142.34713
MonotonicityNot monotonic
2024-09-29T20:20:11.171173image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44872
85.6%
39.89197036 1
 
< 0.1%
63.94964025 1
 
< 0.1%
55.84003296 1
 
< 0.1%
16.1447488 1
 
< 0.1%
59.35526123 1
 
< 0.1%
64.01562928 1
 
< 0.1%
64.33786652 1
 
< 0.1%
28.20182173 1
 
< 0.1%
40.24039019 1
 
< 0.1%
Other values (3173) 3173
 
6.1%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
0 44872
85.6%
0.1825548737 1
 
< 0.1%
0.2069595221 1
 
< 0.1%
0.423073211 1
 
< 0.1%
0.5799610216 1
 
< 0.1%
0.5802986694 1
 
< 0.1%
0.710379838 1
 
< 0.1%
0.8213578002 1
 
< 0.1%
0.8733967713 1
 
< 0.1%
0.9988921061 1
 
< 0.1%
ValueCountFrequency (%)
160.1742683 1
< 0.1%
160.0846504 1
< 0.1%
160.0236342 1
< 0.1%
160.0210732 1
< 0.1%
160.0171183 1
< 0.1%
159.9943072 1
< 0.1%
80.09838357 1
< 0.1%
65.50563378 1
< 0.1%
65.43337873 1
< 0.1%
65.39106423 1
< 0.1%

14616_FERM0101.Biocontainer_Pressure_PV
Real number (ℝ)

MISSING 

Distinct20875
Distinct (%)43.4%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean166.48044
Minimum-17.678558
Maximum480
Zeros0
Zeros (%)0.0%
Negative20580
Negative (%)39.3%
Memory size2.8 MiB
2024-09-29T20:20:11.247537image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-17.678558
5-th percentile-1.4333712
Q1-0.53495697
median0.37180388
Q3480
95-th percentile480
Maximum480
Range497.67856
Interquartile range (IQR)480.53496

Descriptive statistics

Standard deviation228.5384
Coefficient of variation (CV)1.3727643
Kurtosis-1.5866714
Mean166.48044
Median Absolute Deviation (MAD)1.4510844
Skewness0.64286799
Sum8000051.1
Variance52229.801
MonotonicityNot monotonic
2024-09-29T20:20:11.320406image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480 16673
31.8%
-0.09548339844 327
 
0.6%
-0.9664367676 283
 
0.5%
-0.298034668 260
 
0.5%
-0.05497436523 257
 
0.5%
-1.006945801 240
 
0.5%
-0.9461791992 239
 
0.5%
-0.5208312988 235
 
0.4%
-0.5410888672 233
 
0.4%
-0.7233825684 231
 
0.4%
Other values (20865) 29076
55.5%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
-17.67855826 1
< 0.1%
-13.20023193 1
< 0.1%
-12.81082015 1
< 0.1%
-10.91346978 1
< 0.1%
-9.262522964 1
< 0.1%
-9.250579834 1
< 0.1%
-9.196256899 1
< 0.1%
-9.169561768 1
< 0.1%
-9.055902727 1
< 0.1%
-9.039486143 1
< 0.1%
ValueCountFrequency (%)
480 16673
31.8%
363.9071863 1
 
< 0.1%
37.37557373 1
 
< 0.1%
19.14641113 1
 
< 0.1%
19.08400949 1
 
< 0.1%
18.70693948 1
 
< 0.1%
17.97231053 1
 
< 0.1%
16.6753479 1
 
< 0.1%
15.17650757 1
 
< 0.1%
14.9131958 1
 
< 0.1%

14616_FERM0101.DO_1_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct7045
Distinct (%)14.7%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean5.0303294
Minimum0
Maximum113.39003
Zeros37608
Zeros (%)71.8%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:11.392046image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25.138969
Maximum113.39003
Range113.39003
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.980638
Coefficient of variation (CV)2.3816806
Kurtosis16.257979
Mean5.0303294
Median Absolute Deviation (MAD)0
Skewness3.542544
Sum241727.45
Variance143.53568
MonotonicityNot monotonic
2024-09-29T20:20:11.464152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37608
71.8%
15.62647095 16
 
< 0.1%
16.31929016 12
 
< 0.1%
23.46226807 12
 
< 0.1%
15.36104279 12
 
< 0.1%
15.70758667 12
 
< 0.1%
16.25534668 12
 
< 0.1%
16.02679749 11
 
< 0.1%
17.08873749 10
 
< 0.1%
26.81347046 10
 
< 0.1%
Other values (7035) 10339
 
19.7%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
0 37608
71.8%
1.618992043 1
 
< 0.1%
2.053528023 6
 
< 0.1%
2.305600929 1
 
< 0.1%
2.508546638 1
 
< 0.1%
2.516705132 1
 
< 0.1%
2.517918205 1
 
< 0.1%
2.567412758 1
 
< 0.1%
2.593224525 1
 
< 0.1%
2.611331177 4
 
< 0.1%
ValueCountFrequency (%)
113.3900269 1
< 0.1%
113.2430786 1
< 0.1%
113.090686 1
< 0.1%
112.7967896 1
< 0.1%
112.7859009 1
< 0.1%
112.6389526 1
< 0.1%
112.4865601 1
< 0.1%
111.735498 1
< 0.1%
110.6850708 1
< 0.1%
110.6825277 1
< 0.1%

14616_FERM0101.DO_2_PV
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing4359
Missing (%)8.3%
Memory size2.8 MiB
0.0
48053 
64.66350297893688
 
1

Length

Max length17
Median length3
Mean length3.0002913
Min length3

Characters and Unicode

Total characters144176
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48053
91.7%
64.66350297893688 1
 
< 0.1%
(Missing) 4359
 
8.3%

Length

2024-09-29T20:20:11.533475image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T20:20:11.585604image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48053
> 99.9%
64.66350297893688 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 96107
66.7%
. 48054
33.3%
6 4
 
< 0.1%
8 3
 
< 0.1%
3 2
 
< 0.1%
9 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
2 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 96107
66.7%
. 48054
33.3%
6 4
 
< 0.1%
8 3
 
< 0.1%
3 2
 
< 0.1%
9 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
2 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 96107
66.7%
. 48054
33.3%
6 4
 
< 0.1%
8 3
 
< 0.1%
3 2
 
< 0.1%
9 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
2 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 96107
66.7%
. 48054
33.3%
6 4
 
< 0.1%
8 3
 
< 0.1%
3 2
 
< 0.1%
9 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
2 1
 
< 0.1%
7 1
 
< 0.1%

14616_FERM0101.Gas_Overlay_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct28837
Distinct (%)60.0%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean2.4375645
Minimum0
Maximum16.00192
Zeros19158
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:11.640690image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.9997313
Q34.0000727
95-th percentile4.0004599
Maximum16.00192
Range16.00192
Interquartile range (IQR)4.0000727

Descriptive statistics

Standard deviation2.0290296
Coefficient of variation (CV)0.83240037
Kurtosis-0.77695537
Mean2.4375645
Median Absolute Deviation (MAD)0.00058757444
Skewness-0.14842114
Sum117134.73
Variance4.1169613
MonotonicityNot monotonic
2024-09-29T20:20:11.714334image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19158
36.6%
3.999837494 61
 
0.1%
3.99963925 1
 
< 0.1%
4.000073214 1
 
< 0.1%
4.000140437 1
 
< 0.1%
4.000083773 1
 
< 0.1%
3.999912397 1
 
< 0.1%
3.99978926 1
 
< 0.1%
4.000289883 1
 
< 0.1%
3.999781014 1
 
< 0.1%
Other values (28827) 28827
55.0%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
0 19158
36.6%
0.9712903929 1
 
< 0.1%
1.599174768 1
 
< 0.1%
1.599450205 1
 
< 0.1%
1.599482904 1
 
< 0.1%
1.599818691 1
 
< 0.1%
1.59985733 1
 
< 0.1%
1.599864428 1
 
< 0.1%
1.599902071 1
 
< 0.1%
1.599936278 1
 
< 0.1%
ValueCountFrequency (%)
16.00191957 1
< 0.1%
16.0010963 1
< 0.1%
16.00092006 1
< 0.1%
16.00070474 1
< 0.1%
16.00069152 1
< 0.1%
16.00065052 1
< 0.1%
16.00048488 1
< 0.1%
16.0004382 1
< 0.1%
15.99966755 1
< 0.1%
15.99959321 1
< 0.1%

14616_FERM0101.Load_Cell_Net_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct1857
Distinct (%)3.9%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean737.50925
Minimum-27.2
Maximum1696.4
Zeros5713
Zeros (%)10.9%
Negative16651
Negative (%)31.8%
Memory size2.8 MiB
2024-09-29T20:20:11.786936image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-27.2
5-th percentile-18.4
Q1-16.8
median2.0330174
Q31584.8
95-th percentile1665.2
Maximum1696.4
Range1723.6
Interquartile range (IQR)1601.6

Descriptive statistics

Standard deviation801.43662
Coefficient of variation (CV)1.0866801
Kurtosis-1.9583907
Mean737.50925
Median Absolute Deviation (MAD)20.433017
Skewness0.15938066
Sum35440270
Variance642300.66
MonotonicityNot monotonic
2024-09-29T20:20:11.859925image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18 7638
 
14.6%
0 5713
 
10.9%
-18.4 2194
 
4.2%
-1.6 1265
 
2.4%
-17.6 1167
 
2.2%
-1.2 1060
 
2.0%
0.8 750
 
1.4%
0.4 724
 
1.4%
1576.4 534
 
1.0%
-2 461
 
0.9%
Other values (1847) 26548
50.7%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
-27.2 1
 
< 0.1%
-25.6 4
 
< 0.1%
-25.2 182
 
0.3%
-24.8 5
 
< 0.1%
-24.4 2
 
< 0.1%
-24 1
 
< 0.1%
-20 4
 
< 0.1%
-19.2 157
 
0.3%
-18.8 57
 
0.1%
-18.4 2194
4.2%
ValueCountFrequency (%)
1696.4 1
 
< 0.1%
1695.6 2
 
< 0.1%
1695.2 5
< 0.1%
1694.8 7
< 0.1%
1694.4 3
< 0.1%
1694 2
 
< 0.1%
1693.6 4
< 0.1%
1693.205024 1
 
< 0.1%
1693.2 5
< 0.1%
1692.8 6
< 0.1%

14616_FERM0101.pH_1_PV
Real number (ℝ)

MISSING  SKEWED 

Distinct6861
Distinct (%)14.3%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean1.8083132
Minimum-16705.317
Maximum2049.6152
Zeros0
Zeros (%)0.0%
Negative28
Negative (%)0.1%
Memory size2.8 MiB
2024-09-29T20:20:11.932101image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-16705.317
5-th percentile0.0060123444
Q11.5798328
median1.7481483
Q35.8668419
95-th percentile5.9394938
Maximum2049.6152
Range18754.932
Interquartile range (IQR)4.287009

Descriptive statistics

Standard deviation400.58657
Coefficient of variation (CV)221.52499
Kurtosis1643.6758
Mean1.8083132
Median Absolute Deviation (MAD)1.4998914
Skewness-39.499086
Sum86896.683
Variance160469.6
MonotonicityNot monotonic
2024-09-29T20:20:12.006649image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00601234436 2500
 
4.8%
1.748148346 2471
 
4.7%
1.67491951 2081
 
4.0%
1.53050499 1327
 
2.5%
1.705440712 1238
 
2.4%
1.607769585 943
 
1.8%
1.663781166 849
 
1.6%
1.554140282 698
 
1.3%
1.679355812 650
 
1.2%
1.390620041 645
 
1.2%
Other values (6851) 34652
66.1%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
-16705.31719 26
< 0.1%
-10298.18984 1
 
< 0.1%
-0.33400383 1
 
< 0.1%
0.001192850112 1
 
< 0.1%
0.001247440286 1
 
< 0.1%
0.001259128667 1
 
< 0.1%
0.001308620479 1
 
< 0.1%
0.001309268787 1
 
< 0.1%
0.00136826176 1
 
< 0.1%
0.004709544467 1
 
< 0.1%
ValueCountFrequency (%)
2049.615234 1
 
< 0.1%
1950.344922 1
 
< 0.1%
1883.366797 1
 
< 0.1%
1687.218359 1
 
< 0.1%
1554.458887 1
 
< 0.1%
1389.407324 1
 
< 0.1%
1357.113574 1
 
< 0.1%
1355.918262 1
 
< 0.1%
1344.499091 1
 
< 0.1%
1059.302637 4
< 0.1%

14616_FERM0101.pH_2_PV
Real number (ℝ)

MISSING 

Distinct78
Distinct (%)0.2%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean-208.91498
Minimum-389.26096
Maximum3.2
Zeros38
Zeros (%)0.1%
Negative27342
Negative (%)52.2%
Memory size2.8 MiB
2024-09-29T20:20:12.083594image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-389.26096
5-th percentile-389.26096
Q1-389.26096
median-389.26096
Q33.2
95-th percentile3.2
Maximum3.2
Range392.46096
Interquartile range (IQR)392.46096

Descriptive statistics

Standard deviation195.4241
Coefficient of variation (CV)-0.93542407
Kurtosis-1.9733976
Mean-208.91498
Median Absolute Deviation (MAD)0
Skewness0.16149643
Sum-10039201
Variance38190.58
MonotonicityNot monotonic
2024-09-29T20:20:12.161505image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-389.2609619 25933
49.5%
3.2 20652
39.4%
-0.2329555511 1357
 
2.6%
0 38
 
0.1%
-26.87923233 1
 
< 0.1%
1.7517627 1
 
< 0.1%
1.427500248 1
 
< 0.1%
1.243799434 1
 
< 0.1%
-206.8192296 1
 
< 0.1%
-211.8487651 1
 
< 0.1%
Other values (68) 68
 
0.1%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
-389.2609619 25933
49.5%
-389.1811626 1
 
< 0.1%
-387.8849362 1
 
< 0.1%
-385.6619304 1
 
< 0.1%
-385.3407178 1
 
< 0.1%
-379.1654863 1
 
< 0.1%
-377.7421594 1
 
< 0.1%
-336.5587212 1
 
< 0.1%
-333.4207645 1
 
< 0.1%
-314.0435053 1
 
< 0.1%
ValueCountFrequency (%)
3.2 20652
39.4%
2.540166735 1
 
< 0.1%
2.526881296 1
 
< 0.1%
2.525830185 1
 
< 0.1%
2.520545773 1
 
< 0.1%
2.50901713 1
 
< 0.1%
2.4744223 1
 
< 0.1%
1.7517627 1
 
< 0.1%
1.739642704 1
 
< 0.1%
1.709809634 1
 
< 0.1%

14616_FERM0101.PUMP_1_PV
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing4359
Missing (%)8.3%
Memory size2.8 MiB
0.0
48051 
48.0
 
3

Length

Max length4
Median length3
Mean length3.0000624
Min length3

Characters and Unicode

Total characters144165
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48051
91.7%
48.0 3
 
< 0.1%
(Missing) 4359
 
8.3%

Length

2024-09-29T20:20:12.234362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T20:20:12.282568image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48051
> 99.9%
48.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 96105
66.7%
. 48054
33.3%
4 3
 
< 0.1%
8 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 96105
66.7%
. 48054
33.3%
4 3
 
< 0.1%
8 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 96105
66.7%
. 48054
33.3%
4 3
 
< 0.1%
8 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 96105
66.7%
. 48054
33.3%
4 3
 
< 0.1%
8 3
 
< 0.1%

14616_FERM0101.PUMP_1_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct134
Distinct (%)0.3%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean48.465288
Minimum0
Maximum1321.8334
Zeros5004
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:12.338713image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112.4
median34.719998
Q357.03999
95-th percentile143.84005
Maximum1321.8334
Range1321.8334
Interquartile range (IQR)44.63999

Descriptive statistics

Standard deviation60.065844
Coefficient of variation (CV)1.239358
Kurtosis158.68048
Mean48.465288
Median Absolute Deviation (MAD)22.319998
Skewness8.4827052
Sum2328951
Variance3607.9056
MonotonicityNot monotonic
2024-09-29T20:20:12.411312image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5004
 
9.5%
44.63999329 4193
 
8.0%
39.67999573 3251
 
6.2%
22.32000122 2631
 
5.0%
9.919999695 2574
 
4.9%
7.43999939 2483
 
4.7%
12.4 2313
 
4.4%
141.3600464 2035
 
3.9%
32.23999939 1858
 
3.5%
27.28000183 1818
 
3.5%
Other values (124) 19894
38.0%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
0 5004
9.5%
0.8361835807 1
 
< 0.1%
1.317148196 1
 
< 0.1%
1.867436268 1
 
< 0.1%
2.790245837 1
 
< 0.1%
2.952845975 1
 
< 0.1%
3.534282258 1
 
< 0.1%
3.861208527 1
 
< 0.1%
4.011535205 1
 
< 0.1%
4.959999847 105
 
0.2%
ValueCountFrequency (%)
1321.833398 37
 
0.1%
605.4982182 1
 
< 0.1%
560.4793945 41
 
0.1%
248.0002441 3
 
< 0.1%
230.64021 203
 
0.4%
203.3601685 180
 
0.3%
173.6001099 348
0.7%
171.120105 7
 
< 0.1%
158.7200806 648
1.2%
156.2400757 133
 
0.3%

14616_FERM0101.PUMP_2_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct5341
Distinct (%)11.1%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean0.45911219
Minimum0
Maximum48
Zeros42155
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:12.482469image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.3587261
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5957076
Coefficient of variation (CV)3.4756375
Kurtosis44.681691
Mean0.45911219
Median Absolute Deviation (MAD)0
Skewness4.6138661
Sum22062.177
Variance2.5462826
MonotonicityNot monotonic
2024-09-29T20:20:12.555077image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42155
80.4%
8 529
 
1.0%
0.539018631 3
 
< 0.1%
5.412321854 2
 
< 0.1%
0.7388877869 2
 
< 0.1%
0.01567153931 2
 
< 0.1%
1.186093903 2
 
< 0.1%
6.617565155 2
 
< 0.1%
2.491486931 2
 
< 0.1%
7.105562592 2
 
< 0.1%
Other values (5331) 5353
 
10.2%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
0 42155
80.4%
9.830077101 × 10-51
 
< 0.1%
9.950694471 × 10-51
 
< 0.1%
9.992955675 × 10-51
 
< 0.1%
0.0001000500217 1
 
< 0.1%
0.0001004522341 1
 
< 0.1%
0.0001005209783 1
 
< 0.1%
0.0001074923021 1
 
< 0.1%
0.0001211075388 1
 
< 0.1%
0.0001233729154 1
 
< 0.1%
ValueCountFrequency (%)
48 2
 
< 0.1%
8 529
1.0%
7.999999533 1
 
< 0.1%
7.999943062 1
 
< 0.1%
7.999915069 1
 
< 0.1%
7.99986186 1
 
< 0.1%
7.999731674 1
 
< 0.1%
7.99965581 1
 
< 0.1%
7.999316813 1
 
< 0.1%
7.999155488 1
 
< 0.1%

14616_FERM0101.PUMP_2_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct7703
Distinct (%)16.0%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean3825.4147
Minimum0
Maximum17888.144
Zeros11511
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:12.623515image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1150.56382
median4049.3546
Q36959.1758
95-th percentile8473.3062
Maximum17888.144
Range17888.144
Interquartile range (IQR)6808.612

Descriptive statistics

Standard deviation3443.9668
Coefficient of variation (CV)0.90028586
Kurtosis-0.20466211
Mean3825.4147
Median Absolute Deviation (MAD)3082.4559
Skewness0.42573743
Sum1.8382648 × 108
Variance11860907
MonotonicityNot monotonic
2024-09-29T20:20:12.697127image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11511
22.0%
1321.104004 2840
 
5.4%
5933.626953 2695
 
5.1%
7054.5875 2668
 
5.1%
6848.110938 1619
 
3.1%
7503.6375 1363
 
2.6%
7072.089844 1337
 
2.6%
6674.395313 1124
 
2.1%
1354.402637 1075
 
2.1%
5896.459375 907
 
1.7%
Other values (7693) 20915
39.9%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
0 11511
22.0%
1.391127586 3
 
< 0.1%
4.237150192 1
 
< 0.1%
5.280001068 1
 
< 0.1%
12.0999939 1
 
< 0.1%
16.54919434 1
 
< 0.1%
18.11250984 1
 
< 0.1%
21.36007538 1
 
< 0.1%
21.74338379 1
 
< 0.1%
22.62970123 1
 
< 0.1%
ValueCountFrequency (%)
17888.14375 277
0.5%
17872.9375 1
 
< 0.1%
17859.80781 1
 
< 0.1%
17840.00625 1
 
< 0.1%
17803.5875 1
 
< 0.1%
17745.35469 1
 
< 0.1%
17683.875 1
 
< 0.1%
17630.07969 1
 
< 0.1%
17511.95469 1
 
< 0.1%
17434.75469 1
 
< 0.1%

14616_FERM0101.Single_Use_DO_PV
Real number (ℝ)

MISSING 

Distinct9455
Distinct (%)19.7%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean2505.2589
Minimum0
Maximum32921.481
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:12.770691image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.229869
Q1637.64707
median799.99199
Q3799.99199
95-th percentile32921.481
Maximum32921.481
Range32921.481
Interquartile range (IQR)162.34492

Descriptive statistics

Standard deviation7622.6476
Coefficient of variation (CV)3.0426587
Kurtosis11.96454
Mean2505.2589
Median Absolute Deviation (MAD)65.042969
Skewness3.7331117
Sum1.2038771 × 108
Variance58104757
MonotonicityNot monotonic
2024-09-29T20:20:12.841804image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
799.9919922 18827
35.9%
32921.48125 2835
 
5.4%
734.9490234 2589
 
4.9%
538.4612793 2091
 
4.0%
675.1557129 1376
 
2.6%
814.0755371 1319
 
2.5%
816.401709 1066
 
2.0%
689.6482422 858
 
1.6%
650.4528809 755
 
1.4%
719.9351563 662
 
1.3%
Other values (9445) 15676
29.9%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
0 12
< 0.1%
1.115547676 1
 
< 0.1%
1.330806984 1
 
< 0.1%
1.351914883 1
 
< 0.1%
1.470219659 1
 
< 0.1%
1.493391813 1
 
< 0.1%
1.514804086 1
 
< 0.1%
1.542249953 1
 
< 0.1%
1.545021098 1
 
< 0.1%
1.547876167 1
 
< 0.1%
ValueCountFrequency (%)
32921.48125 2835
5.4%
26400.29596 1
 
< 0.1%
917.7376953 196
 
0.4%
916.7911133 110
 
0.2%
877.8580078 115
 
0.2%
816.401709 1066
 
2.0%
814.0755371 1319
2.5%
809.3755008 1
 
< 0.1%
808.8651886 1
 
< 0.1%
807.6954996 1
 
< 0.1%

14616_FERM0101.Single_Use_pH_PV
Real number (ℝ)

MISSING 

Distinct1699
Distinct (%)3.5%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean616.26136
Minimum-794.27197
Maximum800.18398
Zeros0
Zeros (%)0.0%
Negative43
Negative (%)0.1%
Memory size2.8 MiB
2024-09-29T20:20:12.912387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-794.27197
5-th percentile5.7200195
Q1799.68799
median799.87197
Q3799.92798
95-th percentile800.06401
Maximum800.18398
Range1594.456
Interquartile range (IQR)0.23999023

Descriptive statistics

Standard deviation336.03765
Coefficient of variation (CV)0.54528431
Kurtosis-0.22876943
Mean616.26136
Median Absolute Deviation (MAD)0.13603516
Skewness-1.3004982
Sum29613823
Variance112921.3
MonotonicityNot monotonic
2024-09-29T20:20:12.986975image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
799.9199707 4042
 
7.7%
800.0640137 3511
 
6.7%
799.8959961 3250
 
6.2%
799.6959961 3143
 
6.0%
799.7919922 2953
 
5.6%
799.8719727 2196
 
4.2%
5.720019531 2109
 
4.0%
799.6879883 1680
 
3.2%
799.9439941 1225
 
2.3%
799.8160156 1208
 
2.3%
Other values (1689) 22737
43.4%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
-794.2719727 1
 
< 0.1%
-794.1359863 7
< 0.1%
-788.2399902 1
 
< 0.1%
-788.2353152 1
 
< 0.1%
-788.2351994 1
 
< 0.1%
-788.2319824 3
< 0.1%
-788.228793 1
 
< 0.1%
-788.0560059 3
< 0.1%
-788.0399902 2
 
< 0.1%
-788.0159668 2
 
< 0.1%
ValueCountFrequency (%)
800.1839844 565
 
1.1%
800.1600098 448
 
0.9%
800.1279785 267
 
0.5%
800.1120117 1119
 
2.1%
800.0640137 3511
6.7%
800.0560059 1119
 
2.1%
800.0160156 374
 
0.7%
800.0080078 456
 
0.9%
800 509
 
1.0%
799.9919922 351
 
0.7%

14616_FERM0101.Temperatura_PV
Real number (ℝ)

MISSING 

Distinct31119
Distinct (%)64.8%
Missing4359
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean17.610518
Minimum3.0159973
Maximum80.143994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:13.063957image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum3.0159973
5-th percentile3.1952619
Q114.070899
median16.623999
Q325.231995
95-th percentile29.623256
Maximum80.143994
Range77.127997
Interquartile range (IQR)11.161096

Descriptive statistics

Standard deviation8.5911941
Coefficient of variation (CV)0.48784447
Kurtosis-0.79245205
Mean17.610518
Median Absolute Deviation (MAD)5.9576084
Skewness-0.10547597
Sum846255.85
Variance73.808615
MonotonicityNot monotonic
2024-09-29T20:20:13.139171image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.60799561 1070
 
2.0%
29.61600342 867
 
1.7%
3.2 845
 
1.6%
29.58399658 714
 
1.4%
3.208001709 678
 
1.3%
29.56800537 459
 
0.9%
3.176000977 444
 
0.8%
29.63199463 384
 
0.7%
3.223999023 358
 
0.7%
3.167999268 266
 
0.5%
Other values (31109) 41969
80.1%
(Missing) 4359
 
8.3%
ValueCountFrequency (%)
3.015997314 1
 
< 0.1%
3.040002441 1
 
< 0.1%
3.043695288 1
 
< 0.1%
3.046218102 1
 
< 0.1%
3.047998047 3
< 0.1%
3.059195952 1
 
< 0.1%
3.07199707 6
< 0.1%
3.079541592 1
 
< 0.1%
3.080529867 1
 
< 0.1%
3.082664523 1
 
< 0.1%
ValueCountFrequency (%)
80.14399414 1
< 0.1%
30.57431842 1
< 0.1%
30.53599854 1
< 0.1%
30.49449114 1
< 0.1%
30.42865115 1
< 0.1%
30.35999756 1
< 0.1%
30.29066676 1
< 0.1%
30.28740535 1
< 0.1%
30.26400146 1
< 0.1%
30.24799805 1
< 0.1%

Interactions

2024-09-29T20:20:09.371615image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:54.419937image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:57.442532image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:58.447018image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:59.423854image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:00.388812image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:01.386116image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:02.384650image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:03.403363image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:04.413571image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:05.397018image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:06.357635image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:07.356890image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:08.350070image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:09.442449image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:54.522511image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:57.514708image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:58.517242image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:59.492794image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:00.461486image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:05.804768image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:06.785139image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:07.779726image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:19:56.944656image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:57.944521image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:58.933450image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:59.902069image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:00.885875image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:01.884057image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:02.892046image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:03.908656image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:04.909512image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:05.875268image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:06.858967image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:08.867638image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:09.946731image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:57.017974image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:58.020011image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:59.005878image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:59.972175image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:00.960040image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:04.980641image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:05.946027image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:06.932049image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:07.930606image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:08.942801image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:10.015878image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:57.087496image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:58.088711image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:59.073545image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:00.040094image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:01.028954image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:02.027301image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:03.040113image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:04.054538image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:05.048284image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:09.012438image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:10.087064image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:57.152979image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:19:59.143410image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:00.103683image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:01.096067image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:02.094423image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:03.108796image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:04.122382image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:05.113419image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:06.075281image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:07.068659image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:08.066072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:09.079665image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:10.160964image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:19:59.213356image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:04.195530image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:05.184417image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:08.204280image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:19:59.352269image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:00.315527image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:01.312052image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:02.310931image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:03.329246image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:04.340792image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:05.325478image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:06.285593image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:07.282437image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:08.277892image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:09.297653image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Missing values

2024-09-29T20:20:10.386984image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-29T20:20:10.536673image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-29T20:20:10.730807image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

14616_FERM0101.Agitation_PV14616_FERM0101.Air_Sparge_PV14616_FERM0101.Biocontainer_Pressure_PV14616_FERM0101.DO_1_PV14616_FERM0101.DO_2_PV14616_FERM0101.Gas_Overlay_PV14616_FERM0101.Load_Cell_Net_PV14616_FERM0101.pH_1_PV14616_FERM0101.pH_2_PV14616_FERM0101.PUMP_1_PV14616_FERM0101.PUMP_1_TOTAL14616_FERM0101.PUMP_2_PV14616_FERM0101.PUMP_2_TOTAL14616_FERM0101.Single_Use_DO_PV14616_FERM0101.Single_Use_pH_PV14616_FERM0101.Temperatura_PV
DateTime
2023-03-15 00:00:00.00080.00.0000000.71531116.5579930.03.9996391652.8000005.929625-389.2609620.039.6799960.0391.86091317.3611655.87229.607996
2023-03-15 00:15:00.00080.01.6297630.79566315.6343780.04.0001251652.8000005.929625-389.2609620.039.6799960.0391.86091316.4683145.87229.607996
2023-03-15 00:30:00.00080.00.0000000.84432616.6872020.04.0001291652.8072465.929625-389.2609620.039.6799960.0391.86091317.5160345.87229.616003
2023-03-15 00:45:00.00080.00.0000000.75520615.8928020.03.9992681652.8000005.929625-389.2609620.039.6799960.0391.86091316.5950765.87229.631995
2023-03-15 01:00:00.00080.00.0000000.75520616.8211990.03.9998841652.8000005.921457-389.2609620.039.6799960.0391.86091317.7548755.87229.616193
2023-03-15 01:15:00.00080.02.6338470.83540715.6342840.04.0005931652.8000005.921457-389.2609620.039.6799960.0391.86091316.4472185.86429.616003
2023-03-15 01:30:00.00080.00.0000000.83622416.1607930.03.9995861652.8000005.921457-389.2609620.039.6799960.0391.86091316.8902195.86429.607996
2023-03-15 01:45:00.00080.00.0000000.81625216.0267970.03.9994501652.4000005.921457-389.2609620.039.6799960.0391.86091317.3754075.86429.631995
2023-03-15 02:00:00.00080.02.6802670.83663915.4956020.03.9994631652.8000005.913288-389.2609620.039.6799960.0391.86091316.3490675.86429.656006
2023-03-15 02:15:00.00080.00.0000000.81637716.0267970.03.9996951652.4000005.913288-389.2609620.039.6799960.0391.86091316.7284905.85629.631995
14616_FERM0101.Agitation_PV14616_FERM0101.Air_Sparge_PV14616_FERM0101.Biocontainer_Pressure_PV14616_FERM0101.DO_1_PV14616_FERM0101.DO_2_PV14616_FERM0101.Gas_Overlay_PV14616_FERM0101.Load_Cell_Net_PV14616_FERM0101.pH_1_PV14616_FERM0101.pH_2_PV14616_FERM0101.PUMP_1_PV14616_FERM0101.PUMP_1_TOTAL14616_FERM0101.PUMP_2_PV14616_FERM0101.PUMP_2_TOTAL14616_FERM0101.Single_Use_DO_PV14616_FERM0101.Single_Use_pH_PV14616_FERM0101.Temperatura_PV
DateTime
2024-09-10 21:45:00.0000.00.0480.00.00.00.0-17.21.306835-0.2329560.022.3200010.00.0799.991992799.91997115.295185
2024-09-10 22:00:00.0000.00.0480.00.00.00.0-17.21.306835-0.2329560.022.3200010.00.0799.991992799.91997115.191056
2024-09-10 22:15:00.0000.00.0480.00.00.00.0-17.21.306835-0.2329560.022.3200010.00.0799.991992799.91997115.328248
2024-09-10 22:30:00.0000.00.0480.00.00.00.0-17.21.306835-0.2329560.022.3200010.00.0799.991992799.91997115.131252
2024-09-10 22:45:00.0000.00.0480.00.00.00.0-17.21.306835-0.2329560.022.3200010.00.0799.991992799.91997115.176001
2024-09-10 23:00:00.0000.00.0480.00.00.00.0-17.21.306835-0.2329560.022.3200010.00.0799.991992799.91997115.268666
2024-09-10 23:15:00.0000.00.0480.00.00.00.0-17.21.306835-0.2329560.022.3200010.00.0799.991992799.91997115.216003
2024-09-10 23:30:00.0000.00.0480.00.00.00.0-17.21.306835-0.2329560.022.3200010.00.0799.991992799.91997115.169230
2024-09-10 23:45:00.0000.00.0480.00.00.00.0-17.21.306835-0.2329560.022.3200010.00.0799.991992799.91997115.171162
2024-09-11 00:00:00.0000.00.0480.00.00.00.0-17.21.306835-0.2329560.022.3200010.00.0799.991992799.91997115.217546

Duplicate rows

Most frequently occurring

14616_FERM0101.Agitation_PV14616_FERM0101.Air_Sparge_PV14616_FERM0101.Biocontainer_Pressure_PV14616_FERM0101.DO_1_PV14616_FERM0101.DO_2_PV14616_FERM0101.Gas_Overlay_PV14616_FERM0101.Load_Cell_Net_PV14616_FERM0101.pH_1_PV14616_FERM0101.pH_2_PV14616_FERM0101.PUMP_1_PV14616_FERM0101.PUMP_1_TOTAL14616_FERM0101.PUMP_2_PV14616_FERM0101.PUMP_2_TOTAL14616_FERM0101.Single_Use_DO_PV14616_FERM0101.Single_Use_pH_PV14616_FERM0101.Temperatura_PV# duplicates
1295NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4359
20.00.0-0.7841430.00.03.9998370.81.5434683.20.00.0000000.04910.504687799.991992800.01601616.64799861
10890.00.0480.0000000.00.00.0000000.00.0060123.20.044.6399930.01321.10400432921.481250799.91997124.40799616
10830.00.0480.0000000.00.00.0000000.00.0060123.20.044.6399930.01321.10400432921.481250799.91997124.31200015
10900.00.0480.0000000.00.00.0000000.00.0060123.20.044.6399930.01321.10400432921.481250799.91997124.43199515
10800.00.0480.0000000.00.00.0000000.00.0060123.20.044.6399930.01321.10400432921.481250799.91997124.27199713
11380.00.0480.0000000.00.00.0000000.00.0060123.20.044.6399930.01321.10400432921.481250799.91997125.20000013
5480.00.0480.0000000.00.00.000000-18.01.6749203.20.039.6799960.05933.626953538.461279799.69599615.91200012
5790.00.0480.0000000.00.00.000000-18.01.6749203.20.039.6799960.05933.626953538.461279799.69599616.38399712
6280.00.0480.0000000.00.00.000000-18.01.6749203.20.039.6799960.05933.626953538.461279799.69599617.12800312